Ruff: apply same rules as in transformers (#2827)

* Apply same ruff settings as in transformers

See https://github.com/huggingface/transformers/blob/main/pyproject.toml

Co-authored-by: Aaron Gokaslan <aaronGokaslan@gmail.com>

* Apply new style rules

* Style
Co-authored-by: Aaron Gokaslan <aaronGokaslan@gmail.com>

* style

* remove list, ruff wouldn't auto fix.

---------
Co-authored-by: Aaron Gokaslan <aaronGokaslan@gmail.com>

src/diffusers/pipelines/pipeline_flax_utils.py

@@ -491,7 +491,7 @@ class FlaxDiffusionPipeline(ConfigMixin):
         parameters = inspect.signature(obj.__init__).parameters
         required_parameters = {k: v for k, v in parameters.items() if v.default == inspect._empty}
         optional_parameters = set({k for k, v in parameters.items() if v.default != inspect._empty})
-        expected_modules = set(required_parameters.keys()) - set(["self"])
+        expected_modules = set(required_parameters.keys()) - {"self"}
         return expected_modules, optional_parameters
 
     @property

src/diffusers/pipelines/pipeline_utils.py

@@ -204,11 +204,11 @@ def variant_compatible_siblings(filenames, variant=None) -> Union[List[os.PathLi
     non_variant_file_regex = re.compile(f"{'|'.join(weight_names)}")
 
     if variant is not None:
-        variant_filenames = set(f for f in filenames if variant_file_regex.match(f.split("/")[-1]) is not None)
+        variant_filenames = {f for f in filenames if variant_file_regex.match(f.split("/")[-1]) is not None}
     else:
         variant_filenames = set()
 
-    non_variant_filenames = set(f for f in filenames if non_variant_file_regex.match(f.split("/")[-1]) is not None)
+    non_variant_filenames = {f for f in filenames if non_variant_file_regex.match(f.split("/")[-1]) is not None}
 
     usable_filenames = set(variant_filenames)
     for f in non_variant_filenames:
@@ -225,7 +225,7 @@ def warn_deprecated_model_variant(pretrained_model_name_or_path, use_auth_token,
         use_auth_token=use_auth_token,
         revision=None,
     )
-    filenames = set(sibling.rfilename for sibling in info.siblings)
+    filenames = {sibling.rfilename for sibling in info.siblings}
     comp_model_filenames, _ = variant_compatible_siblings(filenames, variant=revision)
     comp_model_filenames = [".".join(f.split(".")[:1] + f.split(".")[2:]) for f in comp_model_filenames]
 
@@ -1115,7 +1115,7 @@ class DiffusionPipeline(ConfigMixin):
             # retrieve all folder_names that contain relevant files
             folder_names = [k for k, v in config_dict.items() if isinstance(v, list)]
 
-            filenames = set(sibling.rfilename for sibling in info.siblings)
+            filenames = {sibling.rfilename for sibling in info.siblings}
             model_filenames, variant_filenames = variant_compatible_siblings(filenames, variant=variant)
 
             # if the whole pipeline is cached we don't have to ping the Hub
@@ -1126,7 +1126,7 @@ class DiffusionPipeline(ConfigMixin):
                     pretrained_model_name, use_auth_token, variant, revision, model_filenames
                 )
 
-            model_folder_names = set([os.path.split(f)[0] for f in model_filenames])
+            model_folder_names = {os.path.split(f)[0] for f in model_filenames}
 
             # all filenames compatible with variant will be added
             allow_patterns = list(model_filenames)
@@ -1157,8 +1157,8 @@ class DiffusionPipeline(ConfigMixin):
             elif use_safetensors and is_safetensors_compatible(model_filenames, variant=variant):
                 ignore_patterns = ["*.bin", "*.msgpack"]
 
-                safetensors_variant_filenames = set([f for f in variant_filenames if f.endswith(".safetensors")])
-                safetensors_model_filenames = set([f for f in model_filenames if f.endswith(".safetensors")])
+                safetensors_variant_filenames = {f for f in variant_filenames if f.endswith(".safetensors")}
+                safetensors_model_filenames = {f for f in model_filenames if f.endswith(".safetensors")}
                 if (
                     len(safetensors_variant_filenames) > 0
                     and safetensors_model_filenames != safetensors_variant_filenames
@@ -1169,8 +1169,8 @@ class DiffusionPipeline(ConfigMixin):
             else:
                 ignore_patterns = ["*.safetensors", "*.msgpack"]
 
-                bin_variant_filenames = set([f for f in variant_filenames if f.endswith(".bin")])
-                bin_model_filenames = set([f for f in model_filenames if f.endswith(".bin")])
+                bin_variant_filenames = {f for f in variant_filenames if f.endswith(".bin")}
+                bin_model_filenames = {f for f in model_filenames if f.endswith(".bin")}
                 if len(bin_variant_filenames) > 0 and bin_model_filenames != bin_variant_filenames:
                     logger.warn(
                         f"\nA mixture of {variant} and non-{variant} filenames will be loaded.\nLoaded {variant} filenames:\n[{', '.join(bin_variant_filenames)}]\nLoaded non-{variant} filenames:\n[{', '.join(bin_model_filenames - bin_variant_filenames)}\nIf this behavior is not expected, please check your folder structure."
@@ -1215,7 +1215,7 @@ class DiffusionPipeline(ConfigMixin):
         parameters = inspect.signature(obj.__init__).parameters
         required_parameters = {k: v for k, v in parameters.items() if v.default == inspect._empty}
         optional_parameters = set({k for k, v in parameters.items() if v.default != inspect._empty})
-        expected_modules = set(required_parameters.keys()) - set(["self"])
+        expected_modules = set(required_parameters.keys()) - {"self"}
         return expected_modules, optional_parameters
 
     @property

src/diffusers/pipelines/repaint/pipeline_repaint.py

@@ -37,7 +37,7 @@ def _preprocess_image(image: Union[List, PIL.Image.Image, torch.Tensor]):
 
     if isinstance(image[0], PIL.Image.Image):
         w, h = image[0].size
-        w, h = map(lambda x: x - x % 8, (w, h))  # resize to integer multiple of 8
+        w, h = (x - x % 8 for x in (w, h))  # resize to integer multiple of 8
 
         image = [np.array(i.resize((w, h), resample=PIL_INTERPOLATION["lanczos"]))[None, :] for i in image]
         image = np.concatenate(image, axis=0)
@@ -58,7 +58,7 @@ def _preprocess_mask(mask: Union[List, PIL.Image.Image, torch.Tensor]):
 
     if isinstance(mask[0], PIL.Image.Image):
         w, h = mask[0].size
-        w, h = map(lambda x: x - x % 32, (w, h))  # resize to integer multiple of 32
+        w, h = (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
         mask = [np.array(m.convert("L").resize((w, h), resample=PIL_INTERPOLATION["nearest"]))[None, :] for m in mask]
         mask = np.concatenate(mask, axis=0)
         mask = mask.astype(np.float32) / 255.0

src/diffusers/pipelines/spectrogram_diffusion/midi_utils.py

@@ -166,7 +166,7 @@ class Codec:
         self._shift_range = EventRange(type="shift", min_value=0, max_value=max_shift_steps)
         self._event_ranges = [self._shift_range] + event_ranges
         # Ensure all event types have unique names.
-        assert len(self._event_ranges) == len(set([er.type for er in self._event_ranges]))
+        assert len(self._event_ranges) == len({er.type for er in self._event_ranges})
 
     @property
     def num_classes(self) -> int:

src/diffusers/pipelines/stable_diffusion/convert_from_ckpt.py

@@ -274,18 +274,18 @@ def create_unet_diffusers_config(original_config, image_size: int, controlnet=Fa
         else:
             raise NotImplementedError(f"Unknown conditional unet num_classes config: {unet_params.num_classes}")
 
-    config = dict(
-        sample_size=image_size // vae_scale_factor,
-        in_channels=unet_params.in_channels,
-        down_block_types=tuple(down_block_types),
-        block_out_channels=tuple(block_out_channels),
-        layers_per_block=unet_params.num_res_blocks,
-        cross_attention_dim=unet_params.context_dim,
-        attention_head_dim=head_dim,
-        use_linear_projection=use_linear_projection,
-        class_embed_type=class_embed_type,
-        projection_class_embeddings_input_dim=projection_class_embeddings_input_dim,
-    )
+    config = {
+        "sample_size": image_size // vae_scale_factor,
+        "in_channels": unet_params.in_channels,
+        "down_block_types": tuple(down_block_types),
+        "block_out_channels": tuple(block_out_channels),
+        "layers_per_block": unet_params.num_res_blocks,
+        "cross_attention_dim": unet_params.context_dim,
+        "attention_head_dim": head_dim,
+        "use_linear_projection": use_linear_projection,
+        "class_embed_type": class_embed_type,
+        "projection_class_embeddings_input_dim": projection_class_embeddings_input_dim,
+    }
 
     if not controlnet:
         config["out_channels"] = unet_params.out_channels
@@ -305,16 +305,16 @@ def create_vae_diffusers_config(original_config, image_size: int):
     down_block_types = ["DownEncoderBlock2D"] * len(block_out_channels)
     up_block_types = ["UpDecoderBlock2D"] * len(block_out_channels)
 
-    config = dict(
-        sample_size=image_size,
-        in_channels=vae_params.in_channels,
-        out_channels=vae_params.out_ch,
-        down_block_types=tuple(down_block_types),
-        up_block_types=tuple(up_block_types),
-        block_out_channels=tuple(block_out_channels),
-        latent_channels=vae_params.z_channels,
-        layers_per_block=vae_params.num_res_blocks,
-    )
+    config = {
+        "sample_size": image_size,
+        "in_channels": vae_params.in_channels,
+        "out_channels": vae_params.out_ch,
+        "down_block_types": tuple(down_block_types),
+        "up_block_types": tuple(up_block_types),
+        "block_out_channels": tuple(block_out_channels),
+        "latent_channels": vae_params.z_channels,
+        "layers_per_block": vae_params.num_res_blocks,
+    }
     return config
 
 

src/diffusers/pipelines/stable_diffusion/pipeline_cycle_diffusion.py

@@ -44,7 +44,7 @@ def preprocess(image):
 
     if isinstance(image[0], PIL.Image.Image):
         w, h = image[0].size
-        w, h = map(lambda x: x - x % 8, (w, h))  # resize to integer multiple of 8
+        w, h = (x - x % 8 for x in (w, h))  # resize to integer multiple of 8
 
         image = [np.array(i.resize((w, h), resample=PIL_INTERPOLATION["lanczos"]))[None, :] for i in image]
         image = np.concatenate(image, axis=0)

src/diffusers/pipelines/stable_diffusion/pipeline_flax_stable_diffusion_controlnet.py

@@ -530,7 +530,7 @@ def unshard(x: jnp.ndarray):
 def preprocess(image, dtype):
     image = image.convert("RGB")
     w, h = image.size
-    w, h = map(lambda x: x - x % 64, (w, h))  # resize to integer multiple of 64
+    w, h = (x - x % 64 for x in (w, h))  # resize to integer multiple of 64
     image = image.resize((w, h), resample=PIL_INTERPOLATION["lanczos"])
     image = jnp.array(image).astype(dtype) / 255.0
     image = image[None].transpose(0, 3, 1, 2)

src/diffusers/pipelines/stable_diffusion/pipeline_flax_stable_diffusion_img2img.py

@@ -520,7 +520,7 @@ def unshard(x: jnp.ndarray):
 
 def preprocess(image, dtype):
     w, h = image.size
-    w, h = map(lambda x: x - x % 32, (w, h))  # resize to integer multiple of 32
+    w, h = (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
     image = image.resize((w, h), resample=PIL_INTERPOLATION["lanczos"])
     image = jnp.array(image).astype(dtype) / 255.0
     image = image[None].transpose(0, 3, 1, 2)

src/diffusers/pipelines/stable_diffusion/pipeline_flax_stable_diffusion_inpaint.py

@@ -563,7 +563,7 @@ def unshard(x: jnp.ndarray):
 
 def preprocess_image(image, dtype):
     w, h = image.size
-    w, h = map(lambda x: x - x % 32, (w, h))  # resize to integer multiple of 32
+    w, h = (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
     image = image.resize((w, h), resample=PIL_INTERPOLATION["lanczos"])
     image = jnp.array(image).astype(dtype) / 255.0
     image = image[None].transpose(0, 3, 1, 2)
@@ -572,7 +572,7 @@ def preprocess_image(image, dtype):
 
 def preprocess_mask(mask, dtype):
     w, h = mask.size
-    w, h = map(lambda x: x - x % 32, (w, h))  # resize to integer multiple of 32
+    w, h = (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
     mask = mask.resize((w, h))
     mask = jnp.array(mask.convert("L")).astype(dtype) / 255.0
     mask = jnp.expand_dims(mask, axis=(0, 1))

src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_img2img.py

@@ -40,7 +40,7 @@ def preprocess(image):
 
     if isinstance(image[0], PIL.Image.Image):
         w, h = image[0].size
-        w, h = map(lambda x: x - x % 64, (w, h))  # resize to integer multiple of 64
+        w, h = (x - x % 64 for x in (w, h))  # resize to integer multiple of 64
 
         image = [np.array(i.resize((w, h), resample=PIL_INTERPOLATION["lanczos"]))[None, :] for i in image]
         image = np.concatenate(image, axis=0)

src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_inpaint_legacy.py

@@ -19,7 +19,7 @@ logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
 
 def preprocess(image):
     w, h = image.size
-    w, h = map(lambda x: x - x % 32, (w, h))  # resize to integer multiple of 32
+    w, h = (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
     image = image.resize((w, h), resample=PIL.Image.LANCZOS)
     image = np.array(image).astype(np.float32) / 255.0
     image = image[None].transpose(0, 3, 1, 2)
@@ -29,7 +29,7 @@ def preprocess(image):
 def preprocess_mask(mask, scale_factor=8):
     mask = mask.convert("L")
     w, h = mask.size
-    w, h = map(lambda x: x - x % 32, (w, h))  # resize to integer multiple of 32
+    w, h = (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
     mask = mask.resize((w // scale_factor, h // scale_factor), resample=PIL.Image.NEAREST)
     mask = np.array(mask).astype(np.float32) / 255.0
     mask = np.tile(mask, (4, 1, 1))

src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_upscale.py

@@ -31,7 +31,7 @@ def preprocess(image):
 
     if isinstance(image[0], PIL.Image.Image):
         w, h = image[0].size
-        w, h = map(lambda x: x - x % 64, (w, h))  # resize to integer multiple of 32
+        w, h = (x - x % 64 for x in (w, h))  # resize to integer multiple of 32
 
         image = [np.array(i.resize((w, h)))[None, :] for i in image]
         image = np.concatenate(image, axis=0)

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_depth2img.py

@@ -41,7 +41,7 @@ def preprocess(image):
 
     if isinstance(image[0], PIL.Image.Image):
         w, h = image[0].size
-        w, h = map(lambda x: x - x % 8, (w, h))  # resize to integer multiple of 8
+        w, h = (x - x % 8 for x in (w, h))  # resize to integer multiple of 8
 
         image = [np.array(i.resize((w, h), resample=PIL_INTERPOLATION["lanczos"]))[None, :] for i in image]
         image = np.concatenate(image, axis=0)
@@ -442,7 +442,7 @@ class StableDiffusionDepth2ImgPipeline(DiffusionPipeline):
         if isinstance(image, PIL.Image.Image):
             image = [image]
         else:
-            image = [img for img in image]
+            image = list(image)
 
         if isinstance(image[0], PIL.Image.Image):
             width, height = image[0].size

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_img2img.py

@@ -78,7 +78,7 @@ def preprocess(image):
 
     if isinstance(image[0], PIL.Image.Image):
         w, h = image[0].size
-        w, h = map(lambda x: x - x % 8, (w, h))  # resize to integer multiple of 8
+        w, h = (x - x % 8 for x in (w, h))  # resize to integer multiple of 8
 
         image = [np.array(i.resize((w, h), resample=PIL_INTERPOLATION["lanczos"]))[None, :] for i in image]
         image = np.concatenate(image, axis=0)

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint_legacy.py

@@ -42,7 +42,7 @@ logger = logging.get_logger(__name__)
 
 def preprocess_image(image):
     w, h = image.size
-    w, h = map(lambda x: x - x % 8, (w, h))  # resize to integer multiple of 8
+    w, h = (x - x % 8 for x in (w, h))  # resize to integer multiple of 8
     image = image.resize((w, h), resample=PIL_INTERPOLATION["lanczos"])
     image = np.array(image).astype(np.float32) / 255.0
     image = image[None].transpose(0, 3, 1, 2)
@@ -54,7 +54,7 @@ def preprocess_mask(mask, scale_factor=8):
     if not isinstance(mask, torch.FloatTensor):
         mask = mask.convert("L")
         w, h = mask.size
-        w, h = map(lambda x: x - x % 8, (w, h))  # resize to integer multiple of 8
+        w, h = (x - x % 8 for x in (w, h))  # resize to integer multiple of 8
         mask = mask.resize((w // scale_factor, h // scale_factor), resample=PIL_INTERPOLATION["nearest"])
         mask = np.array(mask).astype(np.float32) / 255.0
         mask = np.tile(mask, (4, 1, 1))
@@ -76,7 +76,7 @@ def preprocess_mask(mask, scale_factor=8):
         # (potentially) reduce mask channel dimension from 3 to 1 for broadcasting to latent shape
         mask = mask.mean(dim=1, keepdim=True)
         h, w = mask.shape[-2:]
-        h, w = map(lambda x: x - x % 8, (h, w))  # resize to integer multiple of 8
+        h, w = (x - x % 8 for x in (h, w))  # resize to integer multiple of 8
         mask = torch.nn.functional.interpolate(mask, (h // scale_factor, w // scale_factor))
         return mask
 

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_instruct_pix2pix.py

@@ -47,7 +47,7 @@ def preprocess(image):
 
     if isinstance(image[0], PIL.Image.Image):
         w, h = image[0].size
-        w, h = map(lambda x: x - x % 8, (w, h))  # resize to integer multiple of 8
+        w, h = (x - x % 8 for x in (w, h))  # resize to integer multiple of 8
 
         image = [np.array(i.resize((w, h), resample=PIL_INTERPOLATION["lanczos"]))[None, :] for i in image]
         image = np.concatenate(image, axis=0)

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_latent_upscale.py

@@ -38,7 +38,7 @@ def preprocess(image):
 
     if isinstance(image[0], PIL.Image.Image):
         w, h = image[0].size
-        w, h = map(lambda x: x - x % 64, (w, h))  # resize to integer multiple of 64
+        w, h = (x - x % 64 for x in (w, h))  # resize to integer multiple of 64
 
         image = [np.array(i.resize((w, h)))[None, :] for i in image]
         image = np.concatenate(image, axis=0)

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_pix2pix_zero.py

@@ -180,7 +180,7 @@ def preprocess(image):
 
     if isinstance(image[0], PIL.Image.Image):
         w, h = image[0].size
-        w, h = map(lambda x: x - x % 8, (w, h))  # resize to integer multiple of 8
+        w, h = (x - x % 8 for x in (w, h))  # resize to integer multiple of 8
 
         image = [np.array(i.resize((w, h), resample=PIL_INTERPOLATION["lanczos"]))[None, :] for i in image]
         image = np.concatenate(image, axis=0)

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_upscale.py

@@ -37,7 +37,7 @@ def preprocess(image):
 
     if isinstance(image[0], PIL.Image.Image):
         w, h = image[0].size
-        w, h = map(lambda x: x - x % 64, (w, h))  # resize to integer multiple of 64
+        w, h = (x - x % 64 for x in (w, h))  # resize to integer multiple of 64
 
         image = [np.array(i.resize((w, h)))[None, :] for i in image]
         image = np.concatenate(image, axis=0)

src/diffusers/pipelines/versatile_diffusion/pipeline_versatile_diffusion_image_variation.py

@@ -134,7 +134,7 @@ class VersatileDiffusionImageVariationPipeline(DiffusionPipeline):
             return embeds
 
         if isinstance(prompt, torch.Tensor) and len(prompt.shape) == 4:
-            prompt = [p for p in prompt]
+            prompt = list(prompt)
 
         batch_size = len(prompt) if isinstance(prompt, list) else 1